Qwen3-VL 30B A3B Instruct needs ~23.0 GB but Radeon PRO W7600 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
15.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.0 tok/s
TTFT
63909 ms
Safe context
4K
Memory
23.0 GB / 8.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 23.0 GB, but this setup only exposes 8.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.0 tok/s | 34859 ms | 4K |
| Coding | F | Too heavy | 3.0 tok/s | 63909 ms | 4K |
| Agentic Coding | F | Too heavy | 3.0 tok/s | 92958 ms | 4K |
| Reasoning | F | Too heavy | 3.0 tok/s | 75529 ms | 4K |
| RAG | F | Too heavy | 3.0 tok/s | 116198 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for Qwen3-VL 30B A3B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~143 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 142.7 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 119.8 | Offloads |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 108.1 | Offloads |
| RTX 3090 24GB | 24 GB | Q4_K_M | 102.5 | Offloads |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 87.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 72.6 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 68.8 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 53.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 53.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 37.5 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 34.4 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 32.9 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 23.7 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 8.3 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 5.2 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 3.5 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Qwen3-VL 30B A3B Instruct (30B params) fits at each quantization level on Radeon PRO W7600 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 11.7 GB | Low | F0 |
Q3_K_S | 3 | 14.7 GB | Low | F0 |
NVFP4 | 4 | 16.8 GB | Medium | F0 |
Q4_K_M | 4 | 18.3 GB | Medium | F0 |
Q5_K_M | 5 | 21.6 GB | High | F0 |
Q6_K | 6 | 24.6 GB | High | F0 |
Q8_0 | 8 | 32.1 GB | Very High | F0 |
F16 | 16 | 61.5 GB | Maximum | F0 |
升级选项
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,899 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,249 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,499 MSRP
No, Qwen3-VL 30B A3B Instruct requires more memory than Radeon PRO W7600 8GB provides.
Qwen3-VL 30B A3B Instruct (30B parameters) requires approximately 23.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3-VL 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.
On Radeon PRO W7600 8GB, Qwen3-VL 30B A3B Instruct achieves approximately 3.0 tokens per second decode speed with a time-to-first-token of 63909ms using Q4_K_M quantization.
For coding workloads, Qwen3-VL 30B A3B Instruct on Radeon PRO W7600 8GB receives a F grade with 3.0 tok/s and 4K context.
On Radeon PRO W7600 8GB, Qwen3-VL 30B A3B Instruct can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
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